论文标题
从单图像重建新型物体形状的3D重建
3D Reconstruction of Novel Object Shapes from Single Images
论文作者
论文摘要
准确地预测单个图像中任何姿势中任何任意对象的3D形状是计算机视觉研究的关键目标。这是充满挑战的，因为它需要一个模型来学习可以使用有限的训练集来推断任何对象的可见和遮挡部分的表示形式。涵盖所有可能对象形状的训练集本质上是不可行的。这种基于学习的方法本质上容易受到过度拟合的影响，并且成功实施它们是建筑设计和培训方法的函数。我们对影响重建性能和测量的建筑设计，培训，实验设计和评估的因素进行了广泛的研究。我们表明，相对于现有方法类型和OCCNET，我们提出的SDFNET在可见和看不见的形状上实现了最先进的性能。我们为看不见的对象提供了对单图形重建的第一个大规模评估。可以在https://github.com/rehg-lab/3dshapegen上找到源代码，数据和训练有素的模型。
Accurately predicting the 3D shape of any arbitrary object in any pose from a single image is a key goal of computer vision research. This is challenging as it requires a model to learn a representation that can infer both the visible and occluded portions of any object using a limited training set. A training set that covers all possible object shapes is inherently infeasible. Such learning-based approaches are inherently vulnerable to overfitting, and successfully implementing them is a function of both the architecture design and the training approach. We present an extensive investigation of factors specific to architecture design, training, experiment design, and evaluation that influence reconstruction performance and measurement. We show that our proposed SDFNet achieves state-of-the-art performance on seen and unseen shapes relative to existing methods GenRe and OccNet. We provide the first large-scale evaluation of single image shape reconstruction to unseen objects. The source code, data and trained models can be found on https://github.com/rehg-lab/3DShapeGen.
